Can we train machines to think collectively? That is one of the questions that our professor of Global MBA in Digital Business Gonzalo Cuatrecasas will answer in this article as he explores Collaborative Models, Machine Learning and Artificial Intelligence.
Hello and welcome back to one of my parables.
This year, my father passed away, and shortly after, we opened the last will and testament. Some years back, my father remarried a much younger woman and had no children. As you can imagine, the testament favored the widow. The point of all this is that complex problems require unbiased and none-motional analyses, and as such, machines are much better than humans in determining the desired output. In any case, the four siblings agreed to collaborate (Collective Intelligence) and work towards the best option for all (Collective goal). We carefully studied the options and came up with a series of scenarios (Supervised learning). This collaborative effort was incredibly comprehensive and could have only been done with everyon’s input (Regression) and legal assessment (Classification).
Since I like to study these things, I asked myself if a well-trained application could have come up with a similar “Causal Analysis” and predict the best solution? that is, are we at a point where we can train machines to think collectively? The answer is yes. Supervised machine learning algorithms are designed to work on mathematical models that include both inputs and desired outputs.
Digital interaction is shifting our sharing and learning behavior
Humans are great at inserting individual intelligence into shareable constructs. Not only do we share speech, writing and other social behaviors, but we also store, interpret and share what we learn. As we enter the digital age, our sharing and learning behavior is shifting. With technology, we have augmented exponentially the capacity for sharing. This collective ”festival” of information is not only stored in our brains, but also in machines. Therefore, machines now play an increasingly dominant role as our containers of collective intelligence. On the other hand, our necessity to remember and store information diminishes with the availability of vast knowledge at our fingertips.
Machines have two characteristics that make this phenomenon especially interesting – the endless capacity to store information and the ability to correlate information. As this advances, knowledge becomes increasingly distant from individual human minds, and machines transition from mere containers of our collective intelligence to agents capable of using that information on our behalf (Machine learning).
The trust in the human experience vs the reliance in machine algorithms
This social digital interaction, not only is changing humanity’s relationship to individual knowledge and our capacity for collective thinking but also encourages the use of machines for decision-making. On the road ahead, this predisposition to systematic means, will degrade the trust in the human experience and augment the reliance in machine algorithms. A simple example, you are in the car with the family going to a well-known place. This time you turn on Google Maps, which routes you in a different direction than you usually do. As you get to a road split, you have to decide quickly if to rely on Google Maps or on your own experience. You survey the family. What do you think will be the consensus?
Collaborative models, Machine Learning, AI instruments
Machine Learning deals with the construction and training of algorithms, which learn from the data we provide. Such algorithms operate by building models based on correlated and classified inputs. These models automatically adjust with the objective of constantly refining predictions or decisions. Artificial intelligence (AI) is a technology designed to solve complex problems simulating human behavior. As such, machine learning is a subset of AI. Therefore, we clearly see how collaborative models, such as collective thinking, apply to machine learning and will stretch to future AI instruments.
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